{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "11d8810e-c1b2-458c-bf28-ecab44a01cc2",
   "metadata": {
    "tags": []
   },
   "source": [
    "\n",
    "# Number Partition Problem\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "877ae3cf-10cc-4e51-a428-eb57687d8d18",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "In the Number Partitioning Problem [[1](#PartitionWiki)] we need to find how to partition a set of integers into two subsets of equal sums. In case such a partition does not exist, we can ask for a partition where the difference between the sums is minimal.\n",
    "\n",
    "## Mathematical formulation\n",
    "\n",
    "Given a set of numbers $S=\\{s_1,s_2,...,s_n\\}$, a partition is defined as $P_1,P_2 \\subset \\{1,...,n\\}$, with $P_1\\cup P_2=\\{1,...,n\\}$ and $P_1\\cap P_2=\\emptyset$. In the Number Partitioning Problem we need to determine a partition such that $|\\sum_{j\\in P_1}s_j-\\sum_{j\\in P_2}s_j|$ is minimal. A partition can be represented by a binary vector $x$ of size $n$, where we assign 0 or 1 for being in $P_1$ or $P_2$, respectively. The quantity we ask to minimize is $|\\vec{x}\\cdot \\vec{s}-(1-\\vec{x})\\cdot\\vec{s}|=|(2\\vec{x}-1)\\cdot\\vec{s}|$. In practice we will minimize the square of this expression.\n",
    "\n",
    "# Solving with the Classiq platform\n",
    "\n",
    "We go through the steps of solving the problem with the Classiq platform, using QAOA algorithm [[2](#QAOA)]. The solution is based on defining a pyomo model for the optimization problem we would like to solve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "49a9588b-e79e-4813-b7c5-ac068d7b930c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:03:41.367157Z",
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     "shell.execute_reply": "2024-05-07T16:03:42.090734Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from typing import cast\n",
    "\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import pyomo.core as pyo\n",
    "from IPython.display import Markdown, display\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8517ef73-faf6-4fd8-9db8-4088551398e0",
   "metadata": {},
   "source": [
    "## Building the Pyomo model from a graph input\n",
    "\n",
    "We proceed by defining the pyomo model that will be used on the Classiq platform, using the mathematical formulation defined above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "48889b21-557b-481c-80c5-3c0b5c91adb6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:03:42.097597Z",
     "iopub.status.busy": "2024-05-07T16:03:42.095951Z",
     "iopub.status.idle": "2024-05-07T16:03:42.103326Z",
     "shell.execute_reply": "2024-05-07T16:03:42.102649Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# we define a matrix which gets a set of integers s and returns a pyomo model for the partitioning problem\n",
    "\n",
    "\n",
    "def partite(s) -> pyo.ConcreteModel:\n",
    "    model = pyo.ConcreteModel()\n",
    "    SetSize = len(s)  # the set size\n",
    "    model.x = pyo.Var(\n",
    "        range(SetSize), domain=pyo.Binary\n",
    "    )  # our variable is a binary vector\n",
    "\n",
    "    # we define a cost function\n",
    "    model.cost = pyo.Objective(\n",
    "        expr=sum(((2 * model.x[i] - 1) * s[i]) for i in range(SetSize)) ** 2,\n",
    "        sense=pyo.minimize,\n",
    "    )\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "69359e3e-ed85-4b56-9afb-9dd4b9997ada",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:03:42.107922Z",
     "iopub.status.busy": "2024-05-07T16:03:42.106745Z",
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     "shell.execute_reply": "2024-05-07T16:03:42.114863Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is my list:  [4, 8, 11, 7, 1, 8, 8, 5, 7, 11]\n"
     ]
    }
   ],
   "source": [
    "Myset = np.random.randint(1, 12, 10)\n",
    "mylist = [int(x) for x in Myset]\n",
    "print(\"This is my list: \", mylist)\n",
    "set_partition_model = partite(mylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4a985b32-0670-42f3-ba50-5c0097eb75f5",
   "metadata": {
    "execution": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Set Declarations\n",
      "    x_index : Size=1, Index=None, Ordered=Insertion\n",
      "        Key  : Dimen : Domain : Size : Members\n",
      "        None :     1 :    Any :   10 : {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}\n",
      "\n",
      "1 Var Declarations\n",
      "    x : Size=10, Index=x_index\n",
      "        Key : Lower : Value : Upper : Fixed : Stale : Domain\n",
      "          0 :     0 :  None :     1 : False :  True : Binary\n",
      "          1 :     0 :  None :     1 : False :  True : Binary\n",
      "          2 :     0 :  None :     1 : False :  True : Binary\n",
      "          3 :     0 :  None :     1 : False :  True : Binary\n",
      "          4 :     0 :  None :     1 : False :  True : Binary\n",
      "          5 :     0 :  None :     1 : False :  True : Binary\n",
      "          6 :     0 :  None :     1 : False :  True : Binary\n",
      "          7 :     0 :  None :     1 : False :  True : Binary\n",
      "          8 :     0 :  None :     1 : False :  True : Binary\n",
      "          9 :     0 :  None :     1 : False :  True : Binary\n",
      "\n",
      "1 Objective Declarations\n",
      "    cost : Size=1, Index=None, Active=True\n",
      "        Key  : Active : Sense    : Expression\n",
      "        None :   True : minimize : ((2*x[0] - 1)*4 + (2*x[1] - 1)*8 + (2*x[2] - 1)*11 + (2*x[3] - 1)*7 + 2*x[4] - 1 + (2*x[5] - 1)*8 + (2*x[6] - 1)*8 + (2*x[7] - 1)*5 + (2*x[8] - 1)*7 + (2*x[9] - 1)*11)**2\n",
      "\n",
      "3 Declarations: x_index x cost\n"
     ]
    }
   ],
   "source": [
    "set_partition_model.pprint()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17ea14ec-dbb7-487c-b4f1-cabc8d5e3c29",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Setting Up the Classiq Problem Instance\n",
    "\n",
    "In order to solve the Pyomo model defined above, we use the Classiq combinatorial optimization engine. For the quantum part of the QAOA algorithm (`QAOAConfig`) - define the number of repetitions (`num_layers`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "816b468f-a59f-4f2f-8337-4a9d66548425",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:03:42.132929Z",
     "iopub.status.busy": "2024-05-07T16:03:42.131750Z",
     "iopub.status.idle": "2024-05-07T16:03:44.605811Z",
     "shell.execute_reply": "2024-05-07T16:03:44.605152Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from classiq import construct_combinatorial_optimization_model\n",
    "from classiq.applications.combinatorial_optimization import OptimizerConfig, QAOAConfig\n",
    "\n",
    "qaoa_config = QAOAConfig(num_layers=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db34d5ac-6877-4285-8dec-7bf7b37eb783",
   "metadata": {},
   "source": [
    "For the classical optimization part of the QAOA algorithm we define the maximum number of classical iterations (`max_iteration`) and the $\\alpha$-parameter (`alpha_cvar`) for running CVaR-QAOA, an improved variation of the QAOA algorithm [[3](#cvar)]:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e41d0dd3-4135-4330-9ba3-c1b30c339a74",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2024-05-07T16:03:44.608215Z",
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     "shell.execute_reply": "2024-05-07T16:03:44.610977Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "optimizer_config = OptimizerConfig(max_iteration=60, alpha_cvar=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "214d6051-43b8-4b9d-8454-f9cdb62b4cf0",
   "metadata": {},
   "source": [
    "Lastly, we load the model, based on the problem and algorithm parameters, which we can use to solve the problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0243019c-6fc3-435f-b6ec-8b4355d6660c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:03:44.613782Z",
     "iopub.status.busy": "2024-05-07T16:03:44.613487Z",
     "iopub.status.idle": "2024-05-07T16:03:44.714090Z",
     "shell.execute_reply": "2024-05-07T16:03:44.713444Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "qmod = construct_combinatorial_optimization_model(\n",
    "    pyo_model=set_partition_model,\n",
    "    qaoa_config=qaoa_config,\n",
    "    optimizer_config=optimizer_config,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fcc3812-c9d0-421c-84bb-38047297b33f",
   "metadata": {},
   "source": [
    "We also set the quantum backend we want to execute on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "53bc041f-065c-44d2-b220-dafd9d0504ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:03:44.717234Z",
     "iopub.status.busy": "2024-05-07T16:03:44.716619Z",
     "iopub.status.idle": "2024-05-07T16:03:44.733981Z",
     "shell.execute_reply": "2024-05-07T16:03:44.733405Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from classiq import set_execution_preferences\n",
    "from classiq.execution import ClassiqBackendPreferences, ExecutionPreferences\n",
    "\n",
    "backend_preferences = ExecutionPreferences(\n",
    "    backend_preferences=ClassiqBackendPreferences(backend_name=\"aer_simulator\")\n",
    ")\n",
    "\n",
    "qmod = set_execution_preferences(qmod, backend_preferences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7738571f-3a9f-498f-9a15-9c874f3c3dfd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:03:44.736412Z",
     "iopub.status.busy": "2024-05-07T16:03:44.735967Z",
     "iopub.status.idle": "2024-05-07T16:03:44.761670Z",
     "shell.execute_reply": "2024-05-07T16:03:44.761038Z"
    }
   },
   "outputs": [],
   "source": [
    "from classiq import write_qmod\n",
    "\n",
    "write_qmod(qmod, \"set_partition\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "943291f0-6a9f-4286-a69d-ef13a0a12ef6",
   "metadata": {},
   "source": [
    "## Synthesizing the QAOA Circuit and Solving the Problem\n",
    "\n",
    "We can now synthesize and view the QAOA circuit (ansatz) used to solve the optimization problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1d71e29a-5d53-49c4-84b2-45f59be4da31",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:03:44.764221Z",
     "iopub.status.busy": "2024-05-07T16:03:44.763821Z",
     "iopub.status.idle": "2024-05-07T16:03:49.214351Z",
     "shell.execute_reply": "2024-05-07T16:03:49.213612Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening: https://platform.classiq.io/circuit/0ff868cf-2ccd-47e7-b5f7-4500845af8c3?version=0.41.0.dev39%2B79c8fd0855\n"
     ]
    }
   ],
   "source": [
    "from classiq import show, synthesize\n",
    "\n",
    "qprog = synthesize(qmod)\n",
    "show(qprog)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80238cf9-d7bd-46e5-9d48-b7cf23a6b304",
   "metadata": {},
   "source": [
    "We now solve the problem by calling the `execute` function on the quantum program we have generated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "62d12d20-1c80-4a9e-bb6b-b1fddc6cbe40",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:03:49.217154Z",
     "iopub.status.busy": "2024-05-07T16:03:49.216634Z",
     "iopub.status.idle": "2024-05-07T16:03:58.972236Z",
     "shell.execute_reply": "2024-05-07T16:03:58.971471Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from classiq import execute\n",
    "\n",
    "res = execute(qprog).result()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "620ea6a0-cd05-41a9-a2ed-9631c680d2e6",
   "metadata": {},
   "source": [
    "We can check the convergence of the run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "02454398-b229-403c-824a-b1eb539fbc1f",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2024-05-07T16:03:59.019553Z"
    },
    "tags": []
   },
   "outputs": [
    {
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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from classiq.execution import VQESolverResult\n",
    "\n",
    "vqe_result = res[0].value\n",
    "vqe_result.convergence_graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "615ed612-b835-4bf0-aa92-92d30ef8006d",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Optimization Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "670eddd3-2da7-4a88-b571-7884ef24f60c",
   "metadata": {},
   "source": [
    "We can also examine the statistics of the algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "516d78ba-2951-46eb-b1af-efe877513556",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:03:59.023922Z",
     "iopub.status.busy": "2024-05-07T16:03:59.023674Z",
     "iopub.status.idle": "2024-05-07T16:03:59.177765Z",
     "shell.execute_reply": "2024-05-07T16:03:59.177036Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probability</th>\n",
       "      <th>cost</th>\n",
       "      <th>solution</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0, 1, 1, 1, 1, 0, 1, 0, 0, 0]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[1, 1, 1, 0, 0, 0, 0, 1, 1, 0]</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[1, 1, 0, 0, 0, 1, 1, 0, 1, 0]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0, 1, 0, 0, 0, 1, 1, 0, 0, 1]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0, 0, 0, 1, 1, 1, 1, 0, 0, 1]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     probability  cost                        solution  count\n",
       "525        0.001   0.0  [0, 1, 1, 1, 1, 0, 1, 0, 0, 0]      1\n",
       "29         0.004   0.0  [1, 1, 1, 0, 0, 0, 0, 1, 1, 0]      4\n",
       "511        0.001   0.0  [1, 1, 0, 0, 0, 1, 1, 0, 1, 0]      1\n",
       "494        0.001   0.0  [0, 1, 0, 0, 0, 1, 1, 0, 0, 1]      1\n",
       "424        0.001   0.0  [0, 0, 0, 1, 1, 1, 1, 0, 0, 1]      1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from classiq.applications.combinatorial_optimization import (\n",
    "    get_optimization_solution_from_pyo,\n",
    ")\n",
    "\n",
    "solution = get_optimization_solution_from_pyo(\n",
    "    set_partition_model,\n",
    "    vqe_result=vqe_result,\n",
    "    penalty_energy=qaoa_config.penalty_energy,\n",
    ")\n",
    "optimization_result = pd.DataFrame.from_records(solution)\n",
    "optimization_result.sort_values(by=\"cost\", ascending=True).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "687f492b-a4a5-49c6-964c-8959b035bb93",
   "metadata": {},
   "source": [
    "And the histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "31a4e74d-b2b8-42e0-826d-de7b51de1fe8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:03:59.182649Z",
     "iopub.status.busy": "2024-05-07T16:03:59.181402Z",
     "iopub.status.idle": "2024-05-07T16:03:59.666268Z",
     "shell.execute_reply": "2024-05-07T16:03:59.665547Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'cost'}>]], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XxcXFyszMVHV1tTwej5YtW3bYdRwOh1JTU0M/KSkpx1Q0AAAYukZEMrmzs1NNTU0qLS0NjTmdThUUFKihoeGw6x06dEinnXaagsGgzjvvPP3iF7/QWWedddj5fr9ffr8/9Njn80mSAoGAAoFAJCUfUfe23E7TZ9uMhb7sQSx11z1Y6x9s6Hds0e/Yot+xFW2/oz0/DmPMUf9l3rlzp9LS0rR+/Xrl5+eHxm+77TatXbtWGzZs6LFOQ0OD3nvvPU2ZMkUHDhzQ4sWL9dJLL2nr1q065ZRTet1PRUWFFi5c2GN8+fLl8ng8R1suAAAYQB0dHZo5c6YOHDigxMTEo14voldOopGfnx8WZC644AJNnjxZv/vd73TXXXf1uk5paam8Xm/osc/nU3p6uqZPnx7RwX2TQCCg2tpa3bHRKX/Q0Wfb7W9bKgoHuoSodPd72rRpcrlcA13OkEe/Y4t+xxb9jq1o+9195SNSEYWT5ORkxcXFqa2tLWy8ra1NqampR7UNl8ulc889V9u2bTvsHLfbLbfb3eu6/fEk9Acd8ncNnnAy2H8R++s8onf0O7bod2zR79iKtN/RnpuIboiNj49Xdna26urqQmPBYFB1dXVhr44cSVdXl958802NHTs2skoBAMCwEPFlHa/Xqzlz5ignJ0e5ubmqqqpSe3u7iouLJUmzZ89WWlqaKisrJUl33nmnzj//fE2cOFH79+/Xvffeq48++kj//u//3rdHAgAAhoSIw0lRUZH27NmjsrIytba2KisrSzU1NaG3B7e0tMjp/McLMp9++qnmzp2r1tZWjRo1StnZ2Vq/fr0yMzP77igAAMCQEdUNsSUlJSopKel1WX19fdjj++67T/fdd180uwEAAMMQ360DAACsQjgBAABWIZwAAACrEE4AAIBVCCcAAMAqhBMAAGAVwgkAALAK4QQAAFiFcAIAAKxCOAEAAFYhnAAAAKsQTgAAgFUIJwAAwCqEEwAAYBXCCQAAsArhBAAAWIVwAgAArEI4AQAAViGcAAAAqxBOAACAVQgnAADAKoQTAABgFcIJAACwCuEEAABYhXACAACsQjgBAABWIZwAAACrEE4AAIBVCCcAAMAqhBMAAGAVwgkAALAK4QQAAFiFcAIAAKxCOAEAAFYhnAAAAKsQTgAAgFWiCidLly5VRkaGEhISlJeXp8bGxqNab8WKFXI4HLr66quj2S0AABgGIg4nK1eulNfrVXl5uTZt2qSpU6eqsLBQu3fvPuJ627dv13/+53/q4osvjrpYAAAw9EUcTpYsWaK5c+equLhYmZmZqq6ulsfj0bJlyw67TldXl2bNmqWFCxdqwoQJx1QwAAAY2kZEMrmzs1NNTU0qLS0NjTmdThUUFKihoeGw6915550aM2aMrr/+er388svfuB+/3y+/3x967PP5JEmBQECBQCCSko+oe1tup+mzbcZCX/YglrrrHqz1Dzb0O7bod2zR79iKtt/Rnp+IwsnevXvV1dWllJSUsPGUlBS98847va6zbt06Pfzww9q8efNR76eyslILFy7sMb5mzRp5PJ5ISj4qd+UE+3yb/Wn16tUDXcIxqa2tHegShhX6HVv0O7bod2xF2u+Ojo6o9hNROInUwYMH9cMf/lAPPfSQkpOTj3q90tJSeb3e0GOfz6f09HRNnz5diYmJfVZfIBBQbW2t7tjolD/o6LPt9rctFYUDXUJUuvs9bdo0uVyugS5nyKPfsUW/Y4t+x1a0/e6+8hGpiMJJcnKy4uLi1NbWFjbe1tam1NTUHvPff/99bd++XVdeeWVoLBj88lWKESNGqLm5WaeffnqP9dxut9xud49xl8vVL09Cf9Ahf9fgCSeD/Rexv84jeke/Y4t+xxb9jq1I+x3tuYnohtj4+HhlZ2errq4uNBYMBlVXV6f8/Pwe8ydNmqQ333xTmzdvDv1cddVV+ud//mdt3rxZ6enpURUNAACGrogv63i9Xs2ZM0c5OTnKzc1VVVWV2tvbVVxcLEmaPXu20tLSVFlZqYSEBJ199tlh6yclJUlSj3EAAAApinBSVFSkPXv2qKysTK2trcrKylJNTU3oJtmWlhY5nXzwLAAAiE5UN8SWlJSopKSk12X19fVHXPfRRx+NZpcAAGCY4CUOAABgFcIJAACwCuEEAABYhXACAACsQjgBAABWIZwAAACrEE4AAIBVCCcAAMAqhBMAAGAVwgkAALAK4QQAAFiFcAIAAKxCOAEAAFYhnAAAAKsQTgAAgFUIJwAAwCqEEwAAYBXCCQAAsArhBAAAWIVwAgAArEI4AQAAViGcAAAAqxBOAACAVQgnAADAKoQTAABgFcIJAACwCuEEAABYhXACAACsQjgBAABWIZwAAACrEE4AAIBVCCcAAMAqhBMAAGAVwgkAALAK4QQAAFiFcAIAAKwSVThZunSpMjIylJCQoLy8PDU2Nh527lNPPaWcnBwlJSXp+OOPV1ZWlv7whz9EXTAAABjaIg4nK1eulNfrVXl5uTZt2qSpU6eqsLBQu3fv7nX+6NGj9bOf/UwNDQ164403VFxcrOLiYj3//PPHXDwAABh6Ig4nS5Ys0dy5c1VcXKzMzExVV1fL4/Fo2bJlvc6/9NJLdc0112jy5Mk6/fTTNX/+fE2ZMkXr1q075uIBAMDQMyKSyZ2dnWpqalJpaWlozOl0qqCgQA0NDd+4vjFGL7zwgpqbm/XLX/7ysPP8fr/8fn/osc/nkyQFAgEFAoFISj6i7m25nabPthkLfdmDWOque7DWP9jQ79ii37FFv2Mr2n5He34cxpij/su8c+dOpaWlaf369crPzw+N33bbbVq7dq02bNjQ63oHDhxQWlqa/H6/4uLi9Nvf/lY/+tGPDrufiooKLVy4sMf48uXL5fF4jrZcAAAwgDo6OjRz5kwdOHBAiYmJR71eRK+cROuEE07Q5s2bdejQIdXV1cnr9WrChAm69NJLe51fWloqr9cbeuzz+ZSenq7p06dHdHDfJBAIqLa2VndsdMofdPTZdvvblorCgS4hKt39njZtmlwu10CXM+TR79ii37FFv2Mr2n53X/mIVEThJDk5WXFxcWprawsbb2trU2pq6mHXczqdmjhxoiQpKytLb7/9tiorKw8bTtxut9xud49xl8vVL09Cf9Ahf9fgCSeD/Rexv84jeke/Y4t+xxb9jq1I+x3tuYnohtj4+HhlZ2errq4uNBYMBlVXVxd2meebBIPBsHtKAAAAukV8Wcfr9WrOnDnKyclRbm6uqqqq1N7eruLiYknS7NmzlZaWpsrKSklSZWWlcnJydPrpp8vv92v16tX6wx/+oAceeKBvjwQAAAwJEYeToqIi7dmzR2VlZWptbVVWVpZqamqUkpIiSWppaZHT+Y8XZNrb23XzzTfr73//u4477jhNmjRJf/zjH1VUVNR3RwEAAIaMqG6ILSkpUUlJSa/L6uvrwx7ffffduvvuu6PZDQAAGIb4bh0AAGAVwgkAALAK4QQAAFiFcAIAAKxCOAEAAFYhnAAAAKsQTgAAgFUIJwAAwCqEEwAAYBXCCQAAsArhBAAAWIVwAgAArEI4AQAAViGcAAAAqxBOAACAVQgnAADAKoQTAABgFcIJAACwCuEEAABYhXACAACsQjgBAABWIZwAAACrEE4AAIBVCCcAAMAqhBMAAGAVwgkAALAK4QQAAFiFcAIAAKxCOAEAAFYhnAAAAKsQTgAAgFUIJwAAwCqEEwAAYBXCCQAAsArhBAAAWIVwAgAArBJVOFm6dKkyMjKUkJCgvLw8NTY2HnbuQw89pIsvvlijRo3SqFGjVFBQcMT5AABgeIs4nKxcuVJer1fl5eXatGmTpk6dqsLCQu3evbvX+fX19bruuuv04osvqqGhQenp6Zo+fbp27NhxzMUDAIChJ+JwsmTJEs2dO1fFxcXKzMxUdXW1PB6Pli1b1uv8P/3pT7r55puVlZWlSZMm6fe//72CwaDq6uqOuXgAADD0jIhkcmdnp5qamlRaWhoaczqdKigoUENDw1Fto6OjQ4FAQKNHjz7sHL/fL7/fH3rs8/kkSYFAQIFAIJKSj6h7W26n6bNtxkJf9iCWuuserPUPNvQ7tuh3bNHv2Iq239GeH4cx5qj/Mu/cuVNpaWlav3698vPzQ+O33Xab1q5dqw0bNnzjNm6++WY9//zz2rp1qxISEnqdU1FRoYULF/YYX758uTwez9GWCwAABlBHR4dmzpypAwcOKDEx8ajXi+iVk2O1aNEirVixQvX19YcNJpJUWloqr9cbeuzz+UL3qkRycN8kEAiotrZWd2x0yh909Nl2+9uWisKBLiEq3f2eNm2aXC7XQJcz5NHv2KLfsUW/Yyvafndf+YhUROEkOTlZcXFxamtrCxtva2tTamrqEdddvHixFi1apL/+9a+aMmXKEee63W653e4e4y6Xq1+ehP6gQ/6uwRNOBvsvYn+dR/SOfscW/Y4t+h1bkfY72nMT0Q2x8fHxys7ODruZtfvm1q9e5vm6e+65R3fddZdqamqUk5MTVaEAAGB4iPiyjtfr1Zw5c5STk6Pc3FxVVVWpvb1dxcXFkqTZs2crLS1NlZWVkqRf/vKXKisr0/Lly5WRkaHW1lZJ0siRIzVy5Mg+PBQAADAURBxOioqKtGfPHpWVlam1tVVZWVmqqalRSkqKJKmlpUVO5z9ekHnggQfU2dmp73//+2HbKS8vV0VFxbFVDwAAhpyobogtKSlRSUlJr8vq6+vDHm/fvj2aXQAAgGGK79YBAABWIZwAAACrEE4AAIBVCCcAAMAqhBMAAGAVwgkAALAK4QQAAFiFcAIAAKxCOAEAAFYhnAAAAKsQTgAAgFUIJwAAwCqEEwAAYBXCCQAAsArhBAAAWIVwAgAArEI4AQAAViGcAAAAqxBOAACAVQgnAADAKoQTAABgFcIJAACwCuEEAABYhXACAACsQjgBAABWIZwAAACrEE4AAIBVCCcAAMAqhBMAAGAVwgkAALDKiIEuAJHLuH3VQJcQse2LZgx0CQCAQYJXTgAAgFUIJwAAwCqEEwAAYBXCCQAAsArhBAAAWCWqcLJ06VJlZGQoISFBeXl5amxsPOzcrVu36nvf+54yMjLkcDhUVVUVba0AAGAYiDicrFy5Ul6vV+Xl5dq0aZOmTp2qwsJC7d69u9f5HR0dmjBhghYtWqTU1NRjLhgAAAxtEYeTJUuWaO7cuSouLlZmZqaqq6vl8Xi0bNmyXud/+9vf1r333qtrr71Wbrf7mAsGAABDW0QfwtbZ2ammpiaVlpaGxpxOpwoKCtTQ0NBnRfn9fvn9/tBjn88nSQoEAgoEAn22n+5tuZ2mz7aJ3n313PXlOcTh0e/Yot+xRb9jK9p+R3t+Igone/fuVVdXl1JSUsLGU1JS9M4770RVQG8qKyu1cOHCHuNr1qyRx+Pps/10uysn2OfbRLjVq1eH/ru2tnYAKxl+6Hds0e/Yot+xFWm/Ozo6otqPlR9fX1paKq/XG3rs8/mUnp6u6dOnKzExsc/2EwgEVFtbqzs2OuUPOvpsu+hpS0VhqN/Tpk2Ty+Ua6JKGPPodW/Q7tuh3bEXb7+4rH5GKKJwkJycrLi5ObW1tYeNtbW19erOr2+3u9f4Ul8vVL09Cf9AhfxfhpD999bz113lE7+h3bNHv2KLfsRVpv6M9NxHdEBsfH6/s7GzV1dWFxoLBoOrq6pSfnx9VAQAAAF8V8WUdr9erOXPmKCcnR7m5uaqqqlJ7e7uKi4slSbNnz1ZaWpoqKyslfXkT7VtvvRX67x07dmjz5s0aOXKkJk6c2IeHAgAAhoKIw0lRUZH27NmjsrIytba2KisrSzU1NaGbZFtaWuR0/uMFmZ07d+rcc88NPV68eLEWL16sSy65RPX19cd+BAAAYEiJ6obYkpISlZSU9Lrs64EjIyNDxvBWXQAAcHT4bh0AAGAVwgkAALAK4QQAAFiFcAIAAKxCOAEAAFYhnAAAAKsQTgAAgFUIJwAAwCqEEwAAYJWoPiEWiFTG7avkjjO6J1c6u+L5QfMt0NsXzRjoEgBg2OGVEwAAYBXCCQAAsArhBAAAWIVwAgAArEI4AQAAViGcAAAAqxBOAACAVQgnAADAKoQTAABgFcIJAACwCuEEAABYhXACAACsQjgBAABWIZwAAACrEE4AAIBVCCcAAMAqhBMAAGAVwgkAALAK4QQAAFiFcAIAAKxCOAEAAFYhnAAAAKuMGOgCAJtl3L5qoEuI2PZFMwa6hIjRZwBfRTgBhpjuP/TuOKN7cqWzK56Xv8sxwFUBwNHjsg4AALAK4QQAAFglqnCydOlSZWRkKCEhQXl5eWpsbDzi/CeeeEKTJk1SQkKCzjnnHK1evTqqYgEAwNAX8T0nK1eulNfrVXV1tfLy8lRVVaXCwkI1NzdrzJgxPeavX79e1113nSorK/Uv//IvWr58ua6++mpt2rRJZ599dp8cBAAAtuAG72MXcThZsmSJ5s6dq+LiYklSdXW1Vq1apWXLlun222/vMf/Xv/61vvOd7+jWW2+VJN11112qra3Vb37zG1VXVx9j+QAwML7+B2gw3IBs2x8g4HAiCiednZ1qampSaWlpaMzpdKqgoEANDQ29rtPQ0CCv1xs2VlhYqGeeeeaw+/H7/fL7/aHHBw4ckCTt27dPgUAgkpKPKBAIqKOjQyMCTnUF7fzHZCgZETTq6AjS7xih37E1GPr9ySefDHQJfab73+9PPvlELpdroMsJM+KL9oEuIWLf9NyItt8HDx6UJBljIqononCyd+9edXV1KSUlJWw8JSVF77zzTq/rtLa29jq/tbX1sPuprKzUwoULe4yPHz8+knJhoZkDXcAwQ79jy/Z+J/9qoCuArfr7uXHw4EGdeOKJRz3fys85KS0tDXu1JRgMat++fTrppJPkcPTd/5H4fD6lp6fr448/VmJiYp9tF72j37FFv2OLfscW/Y6taPttjNHBgwc1bty4iPYXUThJTk5WXFyc2trawsbb2tqUmpra6zqpqakRzZckt9stt9sdNpaUlBRJqRFJTEzkyR1D9Du26Hds0e/Yot+xFU2/I3nFpFtEbyWOj49Xdna26urqQmPBYFB1dXXKz8/vdZ38/Pyw+ZJUW1t72PkAAGB4i/iyjtfr1Zw5c5STk6Pc3FxVVVWpvb099O6d2bNnKy0tTZWVlZKk+fPn65JLLtGvfvUrzZgxQytWrNDGjRv14IMP9u2RAACAISHicFJUVKQ9e/aorKxMra2tysrKUk1NTeim15aWFjmd/3hB5oILLtDy5cv185//XD/96U91xhln6JlnnrHiM07cbrfKy8t7XEJC/6DfsUW/Y4t+xxb9jq1Y99thIn1/DwAAQD/iu3UAAIBVCCcAAMAqhBMAAGAVwgkAALAK4QQAAFhlWIeTpUuXKiMjQwkJCcrLy1NjY+NAl2S9l156SVdeeaXGjRsnh8PR4wscjTEqKyvT2LFjddxxx6mgoEDvvfde2Jx9+/Zp1qxZSkxMVFJSkq6//nodOnQobM4bb7yhiy++WAkJCUpPT9c999zT34dmpcrKSn3729/WCSecoDFjxujqq69Wc3Nz2JzPP/9c8+bN00knnaSRI0fqe9/7Xo9PZW5padGMGTPk8Xg0ZswY3Xrrrfriiy/C5tTX1+u8886T2+3WxIkT9eijj/b34VnngQce0JQpU0Kfgpmfn6/nnnsutJxe969FixbJ4XDoxz/+cWiMnvediooKORyOsJ9JkyaFllvVazNMrVixwsTHx5tly5aZrVu3mrlz55qkpCTT1tY20KVZbfXq1eZnP/uZeeqpp4wk8/TTT4ctX7RokTnxxBPNM888Y15//XVz1VVXmfHjx5vPPvssNOc73/mOmTp1qnnllVfMyy+/bCZOnGiuu+660PIDBw6YlJQUM2vWLLNlyxbz+OOPm+OOO8787ne/i9VhWqOwsNA88sgjZsuWLWbz5s3miiuuMKeeeqo5dOhQaM6NN95o0tPTTV1dndm4caM5//zzzQUXXBBa/sUXX5izzz7bFBQUmNdee82sXr3aJCcnm9LS0tCcDz74wHg8HuP1es1bb71l7r//fhMXF2dqampierwD7dlnnzWrVq0y7777rmlubjY//elPjcvlMlu2bDHG0Ov+1NjYaDIyMsyUKVPM/PnzQ+P0vO+Ul5ebs846y+zatSv0s2fPntBym3o9bMNJbm6umTdvXuhxV1eXGTdunKmsrBzAqgaXr4eTYDBoUlNTzb333hsa279/v3G73ebxxx83xhjz1ltvGUnm1VdfDc157rnnjMPhMDt27DDGGPPb3/7WjBo1yvj9/tCcBQsWmDPPPLOfj8h+u3fvNpLM2rVrjTFf9tflcpknnngiNOftt982kkxDQ4Mx5stA6XQ6TWtra2jOAw88YBITE0M9vu2228xZZ50Vtq+ioiJTWFjY34dkvVGjRpnf//739LofHTx40JxxxhmmtrbWXHLJJaFwQs/7Vnl5uZk6dWqvy2zr9bC8rNPZ2ammpiYVFBSExpxOpwoKCtTQ0DCAlQ1uH374oVpbW8P6euKJJyovLy/U14aGBiUlJSknJyc0p6CgQE6nUxs2bAjN+ad/+ifFx8eH5hQWFqq5uVmffvppjI7GTgcOHJAkjR49WpLU1NSkQCAQ1vNJkybp1FNPDev5OeecE/oUZ+nLfvp8Pm3dujU056vb6J4znH8furq6tGLFCrW3tys/P59e96N58+ZpxowZPfpCz/vee++9p3HjxmnChAmaNWuWWlpaJNnX62EZTvbu3auurq6wBktSSkqKWltbB6iqwa+7d0fqa2trq8aMGRO2fMSIERo9enTYnN628dV9DEfBYFA//vGPdeGFF4a+/qG1tVXx8fE9vrX76z3/pn4ebo7P59Nnn33WH4djrTfffFMjR46U2+3WjTfeqKefflqZmZn0up+sWLFCmzZtCn0f21fR876Vl5enRx99VDU1NXrggQf04Ycf6uKLL9bBgwet63XE360DYGDMmzdPW7Zs0bp16wa6lCHtzDPP1ObNm3XgwAE9+eSTmjNnjtauXTvQZQ1JH3/8sebPn6/a2lolJCQMdDlD3uWXXx767ylTpigvL0+nnXaa/ud//kfHHXfcAFbW07B85SQ5OVlxcXE97kJua2tTamrqAFU1+HX37kh9TU1N1e7du8OWf/HFF9q3b1/YnN628dV9DDclJSX6v//7P7344os65ZRTQuOpqanq7OzU/v37w+Z/veff1M/DzUlMTLTuH63+Fh8fr4kTJyo7O1uVlZWaOnWqfv3rX9PrftDU1KTdu3frvPPO04gRIzRixAitXbtW//3f/60RI0YoJSWFnvejpKQkfetb39K2bduse34Py3ASHx+v7Oxs1dXVhcaCwaDq6uqUn58/gJUNbuPHj1dqampYX30+nzZs2BDqa35+vvbv36+mpqbQnBdeeEHBYFB5eXmhOS+99JICgUBoTm1trc4880yNGjUqRkdjB2OMSkpK9PTTT+uFF17Q+PHjw5ZnZ2fL5XKF9by5uVktLS1hPX/zzTfDQmFtba0SExOVmZkZmvPVbXTP4ffhy38b/H4/ve4Hl112md58801t3rw59JOTk6NZs2aF/pue959Dhw7p/fff19ixY+17fkd0++wQsmLFCuN2u82jjz5q3nrrLXPDDTeYpKSksLuQ0dPBgwfNa6+9Zl577TUjySxZssS89tpr5qOPPjLGfPlW4qSkJPOXv/zFvPHGG+Zf//Vfe30r8bnnnms2bNhg1q1bZ84444ywtxLv37/fpKSkmB/+8Idmy5YtZsWKFcbj8QzLtxLfdNNN5sQTTzT19fVhb//r6OgIzbnxxhvNqaeeal544QWzceNGk5+fb/Lz80PLu9/+N336dLN582ZTU1NjTj755F7f/nfrrbeat99+2yxdunRYvtXy9ttvN2vXrjUffviheeONN8ztt99uHA6HWbNmjTGGXsfCV9+tYww970s/+clPTH19vfnwww/N3/72N1NQUGCSk5PN7t27jTF29XrYhhNjjLn//vvNqaeeauLj401ubq555ZVXBrok67344otGUo+fOXPmGGO+fDvxHXfcYVJSUozb7TaXXXaZaW5uDtvGJ598Yq677jozcuRIk5iYaIqLi83BgwfD5rz++uvmoosuMm6326SlpZlFixbF6hCt0luvJZlHHnkkNOezzz4zN998sxk1apTxeDzmmmuuMbt27Qrbzvbt283ll19ujjvuOJOcnGx+8pOfmEAgEDbnxRdfNFlZWSY+Pt5MmDAhbB/DxY9+9CNz2mmnmfj4eHPyySebyy67LBRMjKHXsfD1cELP+05RUZEZO3asiY+PN2lpaaaoqMhs27YttNymXjuMMSay11oAAAD6z7C85wQAANiLcAIAAKxCOAEAAFYhnAAAAKsQTgAAgFUIJwAAwCqEEwAAYBXCCQAAsArhBAAAWIVwAgAArEI4AQAAVvl/1P0+NSyutbcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "optimization_result.hist(\"cost\", weights=optimization_result[\"probability\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3a890a1-c5d4-409d-b9a3-d7ffd4fdd6c0",
   "metadata": {},
   "source": [
    "Let us plot the solution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4326e84b-26f6-4ea9-a53b-090fb3658b8c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:03:59.669215Z",
     "iopub.status.busy": "2024-05-07T16:03:59.668565Z",
     "iopub.status.idle": "2024-05-07T16:03:59.672400Z",
     "shell.execute_reply": "2024-05-07T16:03:59.671836Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "best_solution = optimization_result.solution[optimization_result.cost.idxmin()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f349d9fb-132e-4ca0-8433-db1e2d61efa1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:03:59.674917Z",
     "iopub.status.busy": "2024-05-07T16:03:59.674535Z",
     "iopub.status.idle": "2024-05-07T16:03:59.679208Z",
     "shell.execute_reply": "2024-05-07T16:03:59.678640Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P1= [7, 1, 8, 8, 11] , total sum:  35\n",
      "P2= [4, 8, 11, 5, 7] , total sum:  35\n",
      "difference=  0\n"
     ]
    }
   ],
   "source": [
    "p1 = [mylist[i] for i in range(len(mylist)) if best_solution[i] == 0]\n",
    "p2 = [mylist[i] for i in range(len(mylist)) if best_solution[i] == 1]\n",
    "print(\"P1=\", p1, \", total sum: \", sum(p1))\n",
    "print(\"P2=\", p2, \", total sum: \", sum(p2))\n",
    "print(\"difference= \", abs(sum(p1) - sum(p2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dde1905d-aeff-4297-a9d3-ad14910e6161",
   "metadata": {},
   "source": [
    "Lastly, we can compare to the classical solution of the problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5a7ca4b6-25a0-46dd-b5cc-de6a639a6f57",
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     "iopub.status.idle": "2024-05-07T16:03:59.740657Z",
     "shell.execute_reply": "2024-05-07T16:03:59.739961Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model unknown\n",
      "\n",
      "  Variables:\n",
      "    x : Size=10, Index=x_index\n",
      "        Key : Lower : Value : Upper : Fixed : Stale : Domain\n",
      "          0 :     0 :   1.0 :     1 : False : False : Binary\n",
      "          1 :     0 :   1.0 :     1 : False : False : Binary\n",
      "          2 :     0 :   1.0 :     1 : False : False : Binary\n",
      "          3 :     0 :   0.0 :     1 : False : False : Binary\n",
      "          4 :     0 :   1.0 :     1 : False : False : Binary\n",
      "          5 :     0 :   0.0 :     1 : False : False : Binary\n",
      "          6 :     0 :   0.0 :     1 : False : False : Binary\n",
      "          7 :     0 :   0.0 :     1 : False : False : Binary\n",
      "          8 :     0 :   0.0 :     1 : False : False : Binary\n",
      "          9 :     0 :   1.0 :     1 : False : False : Binary\n",
      "\n",
      "  Objectives:\n",
      "    cost : Size=1, Index=None, Active=True\n",
      "        Key  : Active : Value\n",
      "        None :   True :   0.0\n",
      "\n",
      "  Constraints:\n",
      "    None\n"
     ]
    }
   ],
   "source": [
    "from pyomo.opt import SolverFactory\n",
    "\n",
    "solver = SolverFactory(\"couenne\")\n",
    "solver.solve(set_partition_model)\n",
    "\n",
    "set_partition_model.display()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2e5c1b07-6060-455d-81ed-e48a2207c81b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:03:59.743360Z",
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     "shell.execute_reply": "2024-05-07T16:03:59.745852Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "classical_solution = [pyo.value(set_partition_model.x[i]) for i in range(len(mylist))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a7524894-b5c5-42d4-8f92-a019bef5e7da",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:03:59.748666Z",
     "iopub.status.busy": "2024-05-07T16:03:59.748361Z",
     "iopub.status.idle": "2024-05-07T16:03:59.752921Z",
     "shell.execute_reply": "2024-05-07T16:03:59.752289Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P1= [7, 8, 8, 5, 7] , total sum:  35\n",
      "P2= [4, 8, 11, 1, 11] , total sum:  35\n",
      "difference=  0\n"
     ]
    }
   ],
   "source": [
    "p1 = [mylist[i] for i in range(len(mylist)) if classical_solution[i] == 0]\n",
    "p2 = [mylist[i] for i in range(len(mylist)) if classical_solution[i] == 1]\n",
    "print(\"P1=\", p1, \", total sum: \", sum(p1))\n",
    "print(\"P2=\", p2, \", total sum: \", sum(p2))\n",
    "print(\"difference= \", abs(sum(p1) - sum(p2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e82b5953-122a-4707-8ab6-f741f14f13a5",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    "\n",
    "## References\n",
    "\n",
    "<a id='PartitionWiki'>[1]</a>: [Number Partitioning Problem (Wikipedia)](https://en.wikipedia.org/wiki/Partition_problem)\n",
    "\n",
    "<a id='QAOA'>[2]</a>: [Farhi, Edward, Jeffrey Goldstone, and Sam Gutmann. \"A quantum approximate optimization algorithm.\" arXiv preprint arXiv:1411.4028 (2014).](https://arxiv.org/abs/1411.4028)\n",
    "\n",
    "<a id='cvar'>[3]</a>: [Barkoutsos, Panagiotis Kl, et al. \"Improving variational quantum optimization using CVaR.\" Quantum 4 (2020): 256.](https://arxiv.org/abs/1907.04769)\n"
   ]
  }
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